BACKGROUNDGenetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking. METHODSWe used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity. RESULTSProtein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants. CONCLUSIONSThe results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.

In preclinical studies, implanted electrodes can cause severe degradation of MRI images and hence are seldom used for chronic studies employing functional magnetic resonance imaging. In this study, we developed carbon fiber optrodes (optical fiber and electrode hybrid devices), which can be utilised in chronic longitudinal studies aiming to take advantage of emerging optogenetic technologies, and compared them with the more widely used tungsten optrodes. We find that optrodes constructed using small diameter (~130 μm) carbon fiber electrodes cause significantly reduced artifact on functional MRI images compared those made with 50 μm diameter tungsten wire and at the same time the carbon electrodes have lower impedance, which leads to higher quality intracranial LFP recordings. In order to validate this approach, we use these devices to study optogenetically-induced seizure-like afterdischarges in rats sedated with dexmedetomidine and compare these to sub (seizure) threshold stimulations in the same animals. The results indicate that seizure-like afterdischarges involve several extrahippocampal brain regions that are not recruited by subthreshold optogenetic stimulation of the hippocampus at 20 Hz. Subthreshold stimulation led to activation of the entire ipsilateral hippocampus, whereas afterdischarges additionally produced activations in the contralateral hippocampal formation, septum, neocortex, cerebellum, nucleus accumbens, and thalamus. Although we demonstrate just one application, given the ease of fabrication, we anticipate that carbon fiber optrodes could be utilised in a variety of studies that could benefit from longitudinal optogenetic functional magnetic resonance imaging.

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance–a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.

Key Points Question What breast tumor characteristics are associated with rare pathogenic protein truncating or missense variants in breast cancer susceptibility genes? Findings In this case-control study involving 46 387 control participants and 42 680 women with a diagnosis of breast cancer, pathology features (eg, tumor subtype, morphology, size, TNM stage, and lymph node involvement) associated with rare germline (likely) pathogenic variants in 9 different breast cancer susceptibility genes were studied. Substantial differences in tumor subtype distribution by gene were found. Meaning The results of this study suggest that tumor subtypes differ by gene; these findings can potentially inform guidelines for gene panel testing, risk prediction in unaffected individuals, variant classification, and understanding of breast cancer etiology.